Base station identification and positioning method and system, robot and storage medium

Abstract

The invention provides a base station identification and positioning method and system, a robot and a storage medium. The base station is provided with a preset pattern, the preset pattern has a first preset pattern feature and a second preset pattern feature, the first preset pattern feature and the second preset pattern feature are the same or different, and the robot comprises a camera and a laser radar. The base station identification and positioning method comprises the following steps: controlling a camera to shoot to obtain a picture, and controlling a laser radar to scan to obtain laser scanning information; processing the picture to determine a first pattern feature; determining a second pattern feature according to the laser scanning information; when the first pattern feature comprises a first preset pattern feature and the second pattern feature comprises a second preset pattern feature, determining that the base station is identified; when the base station is recognized, pose information of the base station is determined according to the picture and / or the laser scanning information, and the pose information at least comprises the distance between the base station and the robot and / or the azimuth angle of the base station relative to the robot.

Description

Base station identification and positioning method and system, robot and storage medium Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a base station identification and positioning method and system, a robot, and a storage medium. Background Art

[0002] Robots can automatically and intelligently complete pre-set tasks based on specific user needs, improving user convenience, experience, and a sense of technology, making them increasingly popular. Robots are typically powered by rechargeable batteries, but due to capacity limitations, batteries often need to identify and locate a base station before returning to it for recharging.

[0003] Currently, the method for identifying robot base stations is usually to use the camera on the robot to take pictures of the base stations to identify the base stations. However, this base station identification method is not accurate enough and may result in misjudgment.

[0004] Summary of the Invention

[0005] In order to solve the above technical problems, the present application provides a base station identification and positioning method and system, a robot and a storage medium, which can combine a camera and a lidar to identify the preset pattern provided on the base station, thereby improving the recognition accuracy of the base station and avoiding misjudgment.

[0006] The first aspect of the present application provides a base station identification and positioning method, wherein the base station is provided with a preset pattern, the preset pattern having a first preset pattern feature and a second preset pattern feature, the first preset pattern feature and the second preset pattern feature being the same or different, and the robot including a camera and a laser radar. The base station identification and positioning method comprises: controlling the camera to shoot to obtain an image, and controlling the laser radar to scan to obtain laser scanning information; processing the image to determine a first pattern feature; determining a second pattern feature based on the laser scanning information; determining that the base station is identified when the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature; when the base station is identified, determining the position information of the base station based on the image and / or the laser scanning information, the position information at least including the distance between the base station and the robot and / or the azimuth of the base station relative to the robot.

[0007] The second aspect of the present application provides a robot, which is used in conjunction with a base station, and the base station is provided with a preset pattern, the preset pattern having a first preset pattern feature and a second preset pattern feature, and the first preset pattern feature and the second preset pattern feature are the same or different. The robot includes a camera, a laser radar and a control module, the camera is used to take pictures, the laser radar is used to scan and obtain laser scanning information, the control module is used to control the camera to take pictures to obtain pictures, and control the laser radar to scan and obtain laser scanning information, and process the pictures to determine the first pattern feature, determine the second pattern feature based on the laser scanning information, and the control module is used to determine that the base station is identified when the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature, and when the base station is identified, determine the posture information of the base station based on the picture and / or the laser scanning information, the posture information at least including the distance between the base station and the robot and / or the azimuth of the base station relative to the robot.

[0008] A third aspect of the present application provides a base station identification and positioning system, which includes a base station and the aforementioned robot.

[0009] The fourth aspect of the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program is used for execution after being called by a processor to implement the aforementioned base station identification and positioning method. The base station identification and positioning method and system, robot and storage medium provided in this application, by combining the camera and the laser radar to identify the preset pattern provided on the base station, can improve the recognition accuracy when identifying the base station and avoid misjudgment. In addition, it is beneficial for the robot to obtain accurate posture information of the base station so that the robot can accurately reach the base station for charging. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solution of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are some embodiments of the present application. Those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0011] FIG1 is a flow chart of a base station identification and positioning method provided in an embodiment of the present application;

[0012] FIG2 is an application scenario diagram of the base station identification and positioning method;

[0013] FIG3 is a schematic diagram of a curve showing a change trend of a preset reflectivity according to an embodiment of the present application;

[0014] FIG4 is a schematic diagram of the preset pattern in FIG2 ;

[0015] FIG5 is a schematic diagram of a curve of a preset depth change trend provided by an embodiment of the present application;

[0016] FIG6 is a schematic diagram of a curve of a preset depth change trend provided by another embodiment of the present application;

[0017] FIG7 is a schematic diagram of a grayscale change trend curve provided by an embodiment of the present application;

[0018] FIG8 is a schematic diagram of a curve showing a first reflectivity change trend according to an embodiment of the present application;

[0019] FIG9 is a schematic diagram of a curve of a first depth variation trend provided by an embodiment of the present application;

[0020] FIG10 is a schematic diagram of a curve of a first depth variation trend provided by another embodiment of the present application;

[0021] FIG11 is a schematic diagram of a curve showing a laser intensity variation trend according to an embodiment of the present application;

[0022] FIG12 is a schematic diagram of a curve showing a second reflectivity change trend according to an embodiment of the present application;

[0023] FIG13 is a schematic diagram of a curve of a second depth variation trend provided by an embodiment of the present application;

[0024] FIG14 is a schematic diagram of a curve of a second depth variation trend provided by another embodiment of the present application;

[0025] FIG15 is a structural block diagram of a robot provided in one embodiment of the present application;

[0026] FIG16 is a structural block diagram of a base station identification and positioning system provided in an embodiment of the present application.

[0027] Description of main component symbols:

[0028] 200-robot; 100-base station; 10-preset pattern; 11-pattern partition; 11a-first pattern partition; 11b-second pattern partition; 110-laser scanning line; 120-curve of laser intensity change trend; 20-fill light; 210-camera; 220-laser radar; 230-control module; 300-base station identification and positioning system. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] In the description of this application, the terms "first", "second", "third", etc. are used to distinguish different objects rather than to describe a specific order. In addition, the terms "upper", "lower", "inner", "outer", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they cannot be understood as limitations on this application.

[0031] In the description of this application, unless otherwise specified or limited, the term "connection" should be understood in a broad sense. For example, it can mean a fixed connection, a detachable connection, or an integral connection; it can mean a direct connection, an indirect connection through an intermediate medium, or internal communication between two components; it can mean a communication connection; or it can mean an electrical connection. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0032] Please refer to Figures 1 and 2. Figure 1 is a flow chart of the base station identification and positioning method provided in an embodiment of the present application, and Figure 2 is an application scenario diagram of the base station identification and positioning method. As shown in Figure 2, the base station identification and positioning method is applied to a robot 200 to identify and locate a base station 100. The base station 100 is provided with a preset pattern 10. The preset pattern 10 has a first preset pattern feature and a second preset pattern feature. The first preset pattern feature and the second preset pattern feature are the same or different. The robot 200 includes a camera and a laser radar. As shown in Figure 1, the base station identification and positioning method includes the following steps:

[0033] S10: Control the camera to shoot to obtain a picture, and control the laser radar to scan to obtain laser scanning information.

[0034] S20: Process the image to determine a first pattern feature.

[0035] S30: Determine a second pattern feature according to the laser scanning information.

[0036] S40: When the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature, determine that the base station 100 is identified.

[0037] S50: When the base station 100 is identified, the posture information of the base station 100 is determined according to the image and / or the laser scanning information, and the posture information at least includes the distance between the base station 100 and the robot 200 and / or the azimuth angle of the base station 100 relative to the robot 200.

[0038] The base station identification and positioning method provided in the embodiment of the present application determines that the base station 100 has been identified when the first pattern feature acquired by the camera includes the first preset pattern feature and the second pattern feature acquired by the laser radar includes the second preset pattern feature. By combining the camera and the laser radar to identify the preset pattern 10 provided on the base station 100, the recognition accuracy of the base station 100 can be improved and misjudgment can be avoided. In addition, it is beneficial for the robot 200 to obtain accurate position information of the base station 100, so that the robot 200 can accurately reach the base station 100 for charging.

[0039] In some embodiments, as shown in FIG2 , the preset pattern 10 includes at least two pattern partitions 11 , and the at least two pattern partitions 11 satisfy at least one of the first condition, the second condition, and the third condition.

[0040] The first condition includes that the at least two pattern partitions 11 have different reflectivities under the camera's light-sensitive light and the laser light emitted by the laser radar, and the reflectivities change according to a preset reflectivity variation trend. That is, the camera's light-sensitive light has different reflectivities on the at least two pattern partitions 11, and the reflectivities change according to a preset reflectivity variation trend; the laser light emitted by the laser radar has different reflectivities on the at least two pattern partitions 11, and the reflectivities change according to a preset reflectivity variation trend. The camera may be a visible light camera, an infrared camera, an ultraviolet camera, a fluorescent camera, or the like. The visible light camera's light-sensitive light is visible light, and the infrared camera's light-sensitive light is infrared light. The laser radar may be a single-line laser radar, a multi-line laser radar, or the like.

[0041] The at least two pattern subareas 11 have different reflectivities under the photosensitive light, resulting in portions of the image corresponding to the at least two pattern subareas 11 in an image of the preset pattern captured by the camera exhibiting different grayscales. By processing the image captured by the camera to determine a reflectivity variation trend and comparing this reflectivity variation trend with the preset reflectivity variation trend, it can be determined whether the camera has captured the preset pattern 10.

[0042] The laser radar includes a laser transmitter and a laser receiver. The laser transmitter is used to emit laser light. The emitted laser light is reflected by an object and received by the laser receiver. The at least two pattern sections 11 have different reflectivities under the laser light, resulting in different intensities of the reflected laser light received by the laser radar. The laser scanning information obtained by the laser radar can be used to determine the reflectivity change trend. By comparing this reflectivity change trend with the preset reflectivity change trend, it can be determined whether the laser radar has detected the preset pattern 10.

[0043] The second condition includes that the ratio of the sizes of the at least two pattern partitions 11 has a preset size ratio sequence. The size ratio sequence of an image in a picture obtained by photographing the preset pattern 10 is the same as the preset size ratio sequence, and the size ratio sequence determined based on the laser scanning information obtained by the laser radar scanning is the same as the preset size ratio sequence. Thus, when the camera captures a picture, the picture can be processed to determine the size ratio sequence, and the size ratio sequence can be compared with the preset size ratio sequence to determine whether the camera has captured the preset pattern 10. When the laser radar scans to obtain the laser scanning information, the size ratio sequence can be determined based on the laser scanning information, and the size ratio sequence can be compared with the preset size ratio sequence to determine whether the laser radar has detected the preset pattern 10.

[0044] The third condition includes that the depths of the at least two pattern partitions 11 change according to a preset depth change trend, where the depth of the pattern partition 11 is the dimension in a direction perpendicular to the plane in which the pattern partition 11 is located. The depth change trend of the image in the picture obtained by photographing the preset pattern 10 is the same as the preset depth change trend, and the depth change trend determined by the laser scanning information obtained by the lidar scanning is the same as the preset depth change trend. Therefore, when the camera captures a photo, the photo can be processed to determine the depth change trend, and the depth change trend can be compared with the preset depth change trend to determine whether the camera has captured the preset pattern 10. When the lidar scanning obtains the laser scanning information, the distance of the detected object relative to the robot 200 can be determined based on the laser scanning information, that is, the depth change trend of the detected object can be determined, and the depth change trend can be compared with the preset depth change trend to determine whether the lidar has detected the preset pattern 10.

[0045] The first preset pattern feature includes at least one of the preset reflectivity change trend, the preset size ratio sequence and the preset depth change trend, and the second preset pattern feature includes at least one of the preset reflectivity change trend, the preset size ratio sequence and the preset depth change trend.

[0046] In step S40, when the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature, it is determined that the base station 100 is identified, including: when the first pattern feature includes at least one of the preset reflectivity change trend, the preset size ratio sequence and the preset depth change trend, and the second pattern feature includes at least one of the preset reflectivity change trend, the preset size ratio sequence and the preset depth change trend, it is determined that the base station 100 is identified.

[0047] By setting the at least two pattern partitions 11 to satisfy at least one of the first condition, the second condition and the third condition, and by judging whether the first pattern feature and the second pattern feature include at least one of the preset reflectivity change trend, the preset size ratio sequence and the preset depth change trend, the base station 100 can be accurately identified based on at least one of the determined reflectivity change trend, size ratio sequence and depth change trend, thereby further improving the recognition accuracy when identifying the base station 100.

[0048] In some embodiments, as shown in Figure 2, the at least two pattern partitions 11 include at least one first pattern partition 11a and at least one second pattern partition 11b, the reflectivity of the camera's photosensitive light on the at least one first pattern partition 11a is less than the reflectivity on the at least one second pattern partition 11b, the reflectivity of the laser emitted by the lidar on the at least one first pattern partition 11a is less than the reflectivity on the at least one second pattern partition 11b, and the at least one first pattern partition 11a and the at least one second pattern partition 11b are alternately arranged, so that the reflectivity change trend of the at least two pattern partitions 11 under the photosensitive light and the laser is waveform, that is, the preset reflectivity change trend is waveform.

[0049] By alternating the first pattern partitions 11a and the second pattern partitions 11b having different reflectivities, the reflectivities of the at least two pattern partitions 11 under the photosensitive light and the laser can change regularly, which is beneficial for judging whether the first pattern features include the first preset pattern features based on the photos taken by the camera, and is beneficial for judging whether the second pattern features include the second preset pattern features based on the laser scanning information obtained by the lidar scanning.

[0050] When the number of the first pattern partitions 11a is at least two, the reflectivities of the at least two first pattern partitions 11a may be the same or different. When the number of the second pattern partitions 11b is at least two, the reflectivities of the at least two second pattern partitions 11b may be the same or different. The number of the first pattern partitions 11a and the number of the second pattern partitions 11b may be equal or different.

[0051] In some embodiments, as shown in FIG2 , the at least one first pattern partition 11a and the at least one second pattern partition 11b are alternately arranged along a first preset direction (the X direction shown in FIG2 ), such that the curve of the preset reflectivity change trend is a square waveform, that is, the preset reflectivity changes with the change in the size of the preset pattern 10 along the first preset direction, and the curve of the preset reflectivity change trend is a square waveform. Please refer to FIG3 , which is a schematic diagram of a curve of the preset reflectivity change trend provided in one embodiment of the present application. As shown in FIG3 , as the size of the preset pattern 10 along the first preset direction changes, the preset reflectivity exhibits a change characteristic of alternating decrease and increase, wherein the number of peaks in the curve of the preset reflectivity change trend is equal to the number of second pattern partitions 11b, and the number of troughs is equal to the number of first pattern partitions 11a.

[0052] In some embodiments, as shown in FIG2 , the first pattern partition 11a may be in the form of black stripes, and the second pattern partition 11b may be in the form of white stripes. The at least one first pattern partition 11a and the at least one second pattern partition 11b are arranged alternately, i.e., black stripes and white stripes are arranged alternately, and the reflectivity of the black and white stripes changes in a waveform under the camera's light and the laser of the lidar.

[0053] In other embodiments, the first pattern partition 11 a and the second pattern partition 11 b may be other colors and other shapes.

[0054] In some embodiments, as shown in Figure 2, the at least one first pattern partition 11a and the at least one second pattern partition 11b are alternately arranged along the first preset direction, and the width of the first pattern partition 11a and the width of the second pattern partition 11b are the same along the second preset direction (as shown in the Y direction in Figure 2), that is, the width of the first pattern partition 11a is the same in the second preset direction, and the width of the second pattern partition 11b is the same in the second preset direction.

[0055] The second preset direction is perpendicular to the first preset direction, and the width of the first pattern partition 11 a and the width of the second pattern partition 11 b are both dimensions along the first preset direction.

[0056] The preset size ratio sequence is a sequence consisting of the width ratios of every two adjacent pattern partitions 11 in the at least two pattern partitions 11 , that is, a sequence consisting of the width ratios of every two adjacent pattern partitions 11 arranged in sequence.

[0057] For example, please refer to FIG4, which is a schematic diagram of the preset pattern 10 in FIG2. As shown in FIG4, the preset pattern 10 includes at least two first pattern partitions 11a and at least two second pattern partitions 11b. The first pattern partitions 11a and the second pattern partitions 11b are alternately arranged along the first preset direction. The widths of the at least two first pattern partitions 11a arranged along the first preset direction are W and W, respectively. 1a 、W 2a 、W 3a 、W 4a 、W 5a The widths of at least two second pattern partitions 11b arranged along the first preset direction are W 1b 、W 2b 、W 3b 、W 4b The width ratio sequence of the two adjacent pattern partitions 11, that is, the preset size ratio sequence, can be W 1a / W 1b 、W 1b / W 2a 、W 2a / W 2b 、W 2b / W 3a 、W 3a / W 3b 、W 3b / W 4a 、W 4a / W 4b 、W 4b / W 5a , or it can be W 5a / W 4b 、W 4b / W 4a 、W 4a / W 3b 、W 3b / W 3a 、W 3a / W 2b 、W 2b / W 2a 、W 2a / W 1b 、W 1b / W 1a .

[0058] By setting the width of the first pattern partition 11a and the width of the second pattern partition 11b to be the same along the second preset direction, the sequence of the width ratios in the second preset direction is the same, thereby making it possible to determine whether the preset pattern 10 is detected by the laser scanning information obtained by single-line laser radar scanning.

[0059] When there are at least two first pattern partitions 11a, the widths of the at least two first pattern partitions 11a may be the same or different. When there are at least two second pattern partitions 11b, the widths of the at least two second pattern partitions 11b may be the same or different.

[0060] In some embodiments, the depth of the first pattern partition 11a is different from the depth of the second pattern partition 11b, and the at least one first pattern partition 11a and the at least one second pattern partition 11b arranged along the first preset direction vary according to the preset depth variation trend. The depth of the first pattern partition 11a is less than or greater than the depth of the second pattern partition 11b, resulting in the preset pattern 10 having an uneven shape. The curve of the preset depth variation trend is a square waveform, that is, the preset depth value varies with the size of the preset pattern 10 along the first preset direction, and the preset depth variation trend curve is a square waveform.

[0061] For example, please refer to FIG5 , which is a schematic diagram of a curve illustrating a preset depth variation trend according to an embodiment of the present application. As shown in FIG5 , as the size of the preset pattern 10 changes along the first preset direction, the preset depth value exhibits a variation characteristic of alternating decreases and increases. The number of peaks in the curve illustrating the preset depth variation trend is equal to the number of the second pattern subdivisions 11b , and the number of troughs is equal to the number of the first pattern subdivisions 11a ; alternatively, the number of troughs is equal to the number of the second pattern subdivisions 11b , and the number of peaks is equal to the number of the first pattern subdivisions 11a .

[0062] In some embodiments, the depth of the first pattern partition 11 a is equal to the depth of the second pattern partition 11 b , and the first pattern partition 11 a and the second pattern partition 11 b are located in the same plane.

[0063] For example, please refer to FIG6 , which is a schematic diagram of a curve of a preset depth change trend provided in another embodiment of the present application. As shown in FIG6 , as the size of the preset pattern 10 changes along the first preset direction, the preset depth value remains unchanged.

[0064] In some embodiments, the first pattern feature includes at least one of a first reflectivity change trend, a first size ratio sequence, and a first depth change trend. Processing the image to determine the first pattern feature includes: performing grayscale extraction on the image to obtain grayscale information; determining a grayscale change trend based on the grayscale information; determining the first reflectivity change trend and / or the first size ratio sequence based on the grayscale change trend, wherein the first reflectivity change trend is opposite to the grayscale change trend; and / or performing depth extraction on the image to obtain depth information; and determining the first depth change trend based on the depth information.

[0065] When processing the image, the grayscale information and / or the depth information may be obtained, and the first reflectivity variation trend and / or the first size ratio sequence may be determined based on the grayscale information, and / or the first depth variation trend may be determined based on the depth information. The camera may include a 2D camera and / or a 3D camera. Images captured by the 2D camera may be used to obtain grayscale information, and images captured by the 3D camera may be used to obtain depth information.

[0066] In some embodiments, the image captured by the camera is a grayscale image, and grayscale extraction can be performed directly on the grayscale image to obtain grayscale information. In other embodiments, the image captured by the camera is a color image, and the color image can be converted into a grayscale image through color space conversion, and then grayscale extraction can be performed on the grayscale image to obtain grayscale information.

[0067] The grayscale information includes grayscale values ​​and the horizontal positions of pixels corresponding to the grayscale values. Processing the image to obtain grayscale information may include scanning the image row by row to obtain grayscale values ​​of pixels in each row, and then obtaining the grayscale values ​​of pixels in each row and the horizontal positions of pixels corresponding to the grayscale values. Determining the grayscale change trend based on the grayscale information may include drawing a curve of the grayscale change trend based on the obtained grayscale values ​​of pixels in each row and the horizontal positions of pixels corresponding to the grayscale values.

[0068] Determining the first reflectivity change trend based on the grayscale change trend includes: determining the first reflectivity corresponding to the grayscale value of each row of pixels based on a preset correspondence between the grayscale value and the first reflectivity; and plotting a curve representing the first reflectivity change trend based on the determined first reflectivity corresponding to the grayscale value of each row of pixels and the lateral position of each row of pixels. The correspondence between the grayscale value and the first reflectivity defines a negative correlation between the grayscale value and the first reflectivity.

[0069] For example, please refer to FIG7 , which is a schematic diagram of a grayscale change trend curve provided in an embodiment of the present application. As shown in FIG7 , as the lateral position changes, the grayscale value shows a change characteristic of alternating decrease and increase, and the grayscale change trend curve is a square waveform. Please refer to FIG8 , which is a schematic diagram of a first reflectivity change trend curve provided in an embodiment of the present application. Based on the grayscale change trend curve shown in FIG7 , a first reflectivity change trend curve as shown in FIG8 can be obtained. As shown in FIG8 , as the lateral position changes, the first reflectivity shows a change characteristic of alternating decrease and increase, and the first reflectivity change trend curve is a square waveform. The number of peaks in the first reflectivity change trend curve is equal to the number of the second pattern partitions 11b, and the number of troughs is equal to the number of the first pattern partitions 11a.

[0070] In some embodiments, as shown in FIG7 , the grayscale variation trend curve includes at least one peak and at least one trough. Determining the first size ratio sequence based on the grayscale variation trend may include: obtaining a first distance between a starting point and an ending point of each peak of the grayscale variation trend curve, and a second distance between a starting point and an ending point of each trough; and calculating a ratio of the first distance to the second distance between adjacent peaks and troughs to thereby obtain the first size ratio sequence.

[0071] For example, as shown in FIG7 , the first distance of at least one peak of the grayscale change trend curve is S 1a 、S 2a 、S 3a 、S 4a 、S 5a , the second distance of at least one trough is S 1b 、S 2b 、S 3b 、S 4b , the first size ratio sequence S can be obtained by dividing the first distance between the peaks and the troughs by the second distance between the troughs. 1a / S 1b 、S 1b / S 2a 、S 2a / S 2b 、S 2b / S 3a 、S 3a / S 3b 、S 3b / S 4a 、S 4a / S 4b 、S 4b / S 5a , or it can be S 5a / S 4b 、S4b / S 4a 、S 4a / S 3b 、S 3b / S 3a 、S 3a / S 2b 、S 2b / S 2a 、S 2a / S 1b 、S 1b / S 1a . Among them, in the first size ratio sequence is S 1a / S 1b 、S 1b / S 2a 、S 2a / S 2b 、S 2b / S 3a 、S 3a / S 3b 、S 3b / S 4a 、S 4a / S 4b 、S 4b / S 5a When the first size ratio sequence and the preset size ratio sequence W 1a / W 1b 、W 1b / W 2a 、W 2a / W 2b 、W 2b / W 3a 、W 3a / W 3b 、W 3b / W 4a 、W 4a / W 4b 、W 4b / W 5a Same; in the first size ratio sequence is S 5a / S 4b 、S 4b / S 4a 、S 4a / S 3b 、S 3b / S 3a 、S 3a / S 2b 、S 2b / S 2a 、S 2a / S 1b 、S 1b / S 1a When the first size ratio sequence and the preset size ratio sequence W5a / W 4b 、W 4b / W 4a 、W 4a / W 3b 、W 3b / W 3a 、W 3a / W 2b 、W 2b / W 2a 、W 2a / W 1b 、W 1b / W 1a same.

[0072] Among them, the peak of the grayscale change trend curve corresponds to the first pattern partition 11a, the trough of the grayscale change trend corresponds to the second pattern partition 11b, and the ratio of the first distance of the peak to the second distance of the trough is equal to the ratio of the width of the first pattern partition 11a to the width of the second pattern partition 11b. Therefore, it can be determined by the first size ratio sequence whether the camera has captured the preset pattern 10.

[0073] In some embodiments, the depth information includes a depth value and a horizontal position corresponding to the depth value. Determining the first depth change trend based on the depth information may include: scanning the image line by line to obtain a first depth value and a horizontal position corresponding to the first depth value; and drawing a curve of the first depth change trend based on each obtained first depth value and the horizontal position corresponding to the first depth value.

[0074] In some embodiments, the first pattern partition 11 a and the second pattern partition 11 b have different depths, and a curve of the first depth variation trend is drawn in a wavy shape.

[0075] For example, please refer to Figure 9, which is a schematic diagram of a first depth variation trend curve provided in one embodiment of the present application. As shown in Figure 9, as the lateral position changes, the first depth value exhibits a variation characteristic of alternating decreases and increases, and the first depth variation trend curve is square-shaped. The number of peaks in the first depth variation trend curve is equal to the number of second pattern partitions 11b, and the number of troughs is equal to the number of first pattern partitions 11a; alternatively, the number of troughs is equal to the number of second pattern partitions 11b, and the number of peaks is equal to the number of first pattern partitions 11a.

[0076] In some other embodiments, the first pattern partition 11 a and the second pattern partition 11 b have the same depth, and the first depth variation trend curve is a straight line.

[0077] For example, please refer to Figure 10, which is a schematic diagram of a curve of a first depth change trend provided in another embodiment of the present application. As shown in Figure 10, the curve of the first depth change trend is a straight line, and the first depth value remains unchanged with the change of the lateral position.

[0078] The aforementioned determining that the base station 100 is identified when the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature includes: when the first pattern feature satisfies at least one of the following characteristic conditions, the characteristic conditions including: the first reflectivity change trend includes the preset reflectivity change trend, the first size ratio sequence includes the preset size ratio sequence and the first depth change trend, and the second pattern feature includes the second preset pattern feature, determining that the base station 100 is identified.

[0079] By extracting the grayscale information and / or depth information in the image and obtaining at least one of the first reflectivity change trend, the first size ratio sequence and the first depth change trend based on the grayscale information and / or depth information, the accuracy of the camera in recognizing the preset pattern 10 can be improved, which helps to accurately determine whether the camera has captured the preset pattern 10.

[0080] In some embodiments, said performing grayscale extraction on the image includes: performing grayscale extraction on the entire area of ​​the image.

[0081] The entire area of ​​the image may be scanned line by line to obtain grayscale information.

[0082] In other embodiments, the grayscale extraction of the image includes: identifying the image to determine an image area; and performing grayscale extraction on the image area.

[0083] By first determining the image area and then performing grayscale extraction on the image area, the grayscale extraction efficiency can be improved, thereby improving the recognition efficiency of the base station 100.

[0084] The identifying the image to determine the image area may include: identifying the image based on a deep learning method to determine a bounding box, and then determining the image area within the bounding box.

[0085] In some embodiments, the laser scanning information includes a laser intensity value and a scanning distance value, and / or duration information, wherein the duration information includes the interval between the laser radar emitting the laser and receiving the laser, and the second pattern feature includes at least one of a second reflectivity change trend, a second size ratio sequence, and a second depth change trend. In step S30, determining the second pattern feature based on the laser scanning information includes: determining the laser intensity change trend based on the laser intensity value and the scanning distance value; determining the second reflectivity change trend and / or the second size ratio sequence based on the laser intensity change trend, wherein the second reflectivity change trend is the same as the laser intensity change trend; and / or determining the second depth change trend based on the scanning distance value, the duration information, and the propagation speed of the laser.

[0086] That is, the second reflectivity change trend and / or the second size ratio sequence can be determined based on the laser intensity value and scanning distance value in the laser scanning information, and / or the second depth change trend can be determined based on the scanning distance value, duration information and the propagation speed of the laser in the laser scanning information.

[0087] The scanning direction of the laser radar can be parallel to the first preset direction. For example, as shown in FIG2 , the first preset direction is parallel to the ground, and the scanning direction of the laser radar is also parallel to the ground. Thus, the laser radar can scan the preset pattern 10 in a direction parallel to the first preset direction, thereby obtaining laser scanning information in a direction parallel to the first preset direction. The scanning distance value can be the distance that the laser emitted by the laser radar moves when scanning along the scanning direction and acts on the detected object.

[0088] The laser emitter of the laser radar can rotate around a preset center line, which is perpendicular to the ground. The scanning angle range of the laser radar can be 360°.

[0089] In some embodiments, the laser radar may be a single-line laser radar, and the laser scanning line 110 of the single-line laser radar on the preset pattern 10 is shown in Figure 2. The laser scanning line 110 is the intersection of the plane where the laser emitted by the single-line laser radar is located and the preset pattern 10.

[0090] Determining the laser intensity variation trend based on the laser intensity value and the scanning distance value may include plotting a curve 120 (as shown in FIG. 2 ) of the laser intensity variation trend based on the laser intensity value and the scanning distance value. The laser intensity value may be the intensity value of the laser received by the laser receiver, or the difference between the intensity value of the laser received by the laser receiver and the intensity value of the laser emitted by the laser transmitter.

[0091] Determining the second reflectivity change trend based on the laser intensity change trend includes: determining the second reflectivity corresponding to the laser intensity value in the laser scanning information based on a preset correspondence between the laser intensity value and the second reflectivity; and plotting a curve representing the second reflectivity change trend based on the determined second reflectivity and the scanning distance value. The correspondence between the laser intensity value and the second reflectivity defines a positive correlation between the laser intensity value and the second reflectivity.

[0092] For example, please refer to FIG11 , which is a schematic diagram of a curve 120 showing a laser intensity variation trend according to an embodiment of the present application. As shown in FIG11 , as the scanning distance changes, the laser intensity value exhibits a characteristic of alternating decreases and increases, and the laser intensity variation trend curve 120 is square-shaped. The number of peaks in the laser intensity variation trend curve 120 is equal to the number of the second pattern subareas 11b, and the number of troughs is equal to the number of the first pattern subareas 11a.

[0093] Please refer to Figure 12, which is a schematic diagram of a curve showing a second reflectivity variation trend according to an embodiment of the present application. The second reflectivity variation trend curve shown in Figure 12 is derived based on the laser intensity variation trend curve 120 shown in Figure 11. As shown in Figure 12, the second reflectivity exhibits alternating decreases and increases as the scanning distance changes, and the curve showing the second reflectivity variation trend exhibits a square waveform. The number of peaks in the curve equals the number of second pattern sub-areas 11b, and the number of valleys equals the number of first pattern sub-areas 11a.

[0094] In some embodiments, as shown in FIG11 , the laser variation trend curve includes at least one peak and at least one trough. Determining the second size ratio sequence based on the laser variation trend may include: obtaining a third distance between a starting point and an ending point of each peak of the laser variation trend curve, and a fourth distance between a starting point and an ending point of each trough; and calculating a ratio of the third distance to the fourth distance between adjacent peaks and troughs to thereby obtain the second size ratio sequence.

[0095] For example, as shown in FIG11 , the fourth distance of at least one trough of the laser variation trend curve is D 1a 、D 2a 、D 3a 、D 4a 、D 5a , the third distance of at least one crest is D 1b 、D 2b 、D 3b 、D4b , the fourth distance between the troughs of adjacent peaks and troughs is divided by the third distance between the peaks, and the second size ratio sequence can be obtained as D 1a / D 1b 、D 1b / D 2a 、D 2a / D 2b 、D 2b / D 3a 、D 3a / D 3b 、D 3b / D 4a 、D 4a / D 4b 、D 4b / D 5a , or it can be D 5a / D 4b 、D 4b / D 4a 、D 4a / D 3b 、D 3b / D 3a 、D 3a / D 2b 、D 2b / D 2a 、D 2a / D 1b 、D 1b / D 1a Among them, the second size ratio sequence is D 1a / D 1b 、D 1b / D 2a 、D 2a / D 2b 、D 2b / D 3a 、D 3a / D 3b 、D 3b / D 4a 、D 4a / D 4b 、D 4b / D 5a When the second size ratio sequence is equal to the preset size ratio sequence W 1a / W 1b 、W 1b / W 2a 、W 2a / W 2b 、W 2b / W 3a 、W 3a / W 3b 、W 3b / W 4a 、W4a / W 4b 、W 4b / W 5a Same; in the second size ratio sequence is D 5a / D 4b 、D 4b / D 4a 、D 4a / D 3b 、D 3b / D 3a 、D 3a / D 2b 、D 2b / D 2a 、D 2a / D 1b 、D 1b / D 1a When the second size ratio sequence is equal to the preset size ratio sequence W 5a / W 4b 、W 4b / W 4a 、W 4a / W 3b 、W 3b / W 3a 、W 3a / W 2b 、W 2b / W 2a 、W 2a / W 1b 、W 1b / W 1a same.

[0096] Among them, the peak of the curve of the laser change trend corresponds to the second pattern partition 11b, the trough of the laser change trend corresponds to the first pattern partition 11a, and the ratio of the third distance of the peak to the fourth distance of the trough is equal to the ratio of the width of the second pattern partition 11b to the width of the first pattern partition 11a. Therefore, the second size ratio sequence can be used to determine whether the laser radar detects the preset pattern 10.

[0097] In some embodiments, determining the second depth change trend based on the scanning distance value, the duration information, and the propagation speed of the laser includes: calculating the second depth value based on the interval duration and the propagation speed of the laser; and drawing a curve of the second depth change trend based on the calculated second depth value and the scanning distance value.

[0098] In some embodiments, the first pattern partition 11 a and the second pattern partition 11 b have different depths, and a curve of the second depth variation trend is drawn in a wavy shape.

[0099] For example, please refer to Figure 13, which is a schematic diagram of a curve of a second depth variation trend provided in an embodiment of the present application. As shown in Figure 13, as the scanning distance value changes, the second depth value exhibits a variation characteristic of alternating decreases and increases, and the curve of the second depth variation trend is square-shaped. The number of peaks in the curve of the second depth variation trend is equal to the number of the second pattern partitions 11b, and the number of troughs is equal to the number of the first pattern partitions 11a; alternatively, the number of troughs is equal to the number of the second pattern partitions 11b, and the number of peaks is equal to the number of the first pattern partitions 11a.

[0100] In some other embodiments, the first pattern partition 11 a and the second pattern partition 11 b have the same depth, and the curve of the second depth variation trend is a straight line.

[0101] For example, please refer to Figure 14, which is a schematic diagram of a curve of the second depth change trend provided in another embodiment of the present application. As shown in Figure 14, the curve of the second depth change trend is a straight line, and the second depth value remains unchanged as the scanning distance value changes.

[0102] The aforementioned determining that the base station 100 is identified when the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature includes: when the second pattern feature satisfies at least one of the following characteristic conditions, the characteristic conditions including: the second reflectivity change trend includes the preset reflectivity change trend, the second size ratio sequence includes the preset size ratio sequence and the second depth change trend, and the first pattern feature includes the first preset pattern feature, determining that the base station 100 is identified.

[0103] By obtaining at least one of the second reflectivity change trend, the second size ratio sequence and the second depth change trend based on the laser scanning information, the recognition accuracy of the laser radar when identifying the preset pattern 10 can be improved, which helps to accurately determine whether the laser radar detects the preset pattern 10.

[0104] In some embodiments, determining the position information of the base station 100 based on the image and / or the laser scanning information includes: determining the azimuth angle of the base station 100 relative to the robot 200 based on at least the image and the actual size of the preset pattern 10; and determining the distance between the base station 100 and the robot 200 based on the laser scanning information. The actual size of the preset pattern 10 may be pre-stored in a memory of the robot 200.

[0105] The azimuth angle may be the angle between the base station 100 and a predetermined direction, where the predetermined direction may be, for example, true north, or the forward direction of the robot 200. Determining the azimuth angle of the base station 100 relative to the robot 200 based at least on the actual size of the image and the preset pattern 10 includes performing feature matching on the image and the preset pattern 10 using a line-to-line matching image algorithm to determine the azimuth angle of the base station 100 relative to the robot 200. Specifically, feature extraction is performed on the image and the preset pattern 10, such as by using a Hough transform enhancement algorithm to extract features, and feature matching is performed on the features extracted from the image and the features extracted from the preset pattern 10, such as by using a least squares method and a RANSAC algorithm. The azimuth angle of the base station 100 relative to the robot 200 is obtained through matching points.

[0106] In which, the laser scanning information includes the aforementioned time information and the propagation speed of the laser; determining the distance between the base station 100 and the robot 200 based on the laser scanning information includes: determining that the distance between the base station 100 and the robot 200 is equal to half of the product of the interval duration and the propagation speed of the laser.

[0107] In some embodiments, as shown in FIG2 , the base station 100 includes a fill light 20, which is configured to emit light with gradually varying brightness to adjust the ambient light brightness of the preset pattern 10. In step S10, controlling the camera to capture a picture includes: controlling the camera to capture a picture and obtaining a corresponding capture time.

[0108] The fill light 20 can periodically adjust its brightness, for example, gradually increasing from brightness L1 to brightness L2 and then gradually decreasing back to brightness L1. The base station 100 records the adjustment time when the fill light 20 adjusts its brightness. In other embodiments, the fill light 20 can also change its brightness arbitrarily.

[0109] By providing a fill light 20 that emits light with gradually changing brightness, overexposure / underexposure of the image taken by the camera caused by changes in the brightness of the ambient light can be avoided, which is beneficial to the determination of the first pattern feature.

[0110] The base station identification and positioning method also includes: when the base station 100 is identified based on the first pattern feature of a certain picture, the shooting time of the picture is sent to the base station 100, so that the base station 100 determines the brightness of the fill light 20 corresponding to the shooting time, and controls and adjusts the brightness of the fill light 20 to the brightness corresponding to the shooting time.

[0111] Exemplarily, the camera captures picture A and records the shooting time t. When the base station 100 is identified based on the first pattern feature of picture A, the robot 200 sends the shooting time t to the base station 100. The base station 100 determines the brightness of the fill light 20 at that moment based on the shooting time t, and controls the brightness of the fill light 20 to be adjusted to the brightness at that moment.

[0112] Among them, by controlling the brightness of the fill light 20 to maintain the brightness corresponding to the shooting moment, after the robot 200 recognizes the base station 100, when continuously taking pictures while driving into the base station 100, the pictures taken can be prevented from being overexposed or underexposed. Furthermore, it is beneficial for the robot 200 to continuously recognize the base station 100 and obtain the accurate azimuth angle of the base station 100 relative to the robot 200, so as to facilitate the robot 200 to enter the station.

[0113] The robot 200 is in communication connection with the base station 100 , as indicated by the bidirectional arrows in FIG2 .

[0114] Among them, when the base station 100 controls the brightness of the fill light 20 to maintain the brightness corresponding to the shooting moment, the camera continuously takes pictures, and the laser radar continuously scans to obtain laser scanning information. The control module of the robot 200 processes the photos continuously taken by the camera to determine the first pattern features, and calculates the azimuth of the base station 100 relative to the robot 200 based on the pictures. The control module determines the second pattern features based on the laser scanning information obtained by the laser radar scanning, and determines the distance between the base station 100 and the robot 200 based on the laser scanning information. Then, it is further determined that the base station 100 is identified, and the distance and azimuth of the base station 100 relative to the robot 200 are continuously obtained during the driving process of the robot 200, so as to plan and continuously correct the driving path of the robot 200 into the base station 100, thereby facilitating the robot 200 to enter the base station 100 accurately.

[0115] Please refer to Figure 15, which is a block diagram of a robot 200 according to an embodiment of the present application. The robot 200 is used in conjunction with a base station 100. As mentioned above, the base station 100 is provided with a preset pattern 10 having a first preset pattern feature and a second preset pattern feature. As shown in Figure 15, the robot 200 includes a camera 210, a laser radar 220 and a control module 230. The camera 210 is used to take pictures, and the laser radar 220 is used to scan to obtain laser scanning information. The control module 230 is used to control the camera 210 to take pictures to obtain pictures, and control the laser radar 220 to scan to obtain laser scanning information, and process the pictures to determine the first pattern features, and determine the second pattern features according to the laser scanning information. The control module 230 is also used to determine that the base station 100 is identified when the first pattern features include the first preset pattern features and the second pattern features include the second preset pattern features, and when the base station 100 is identified, determine the posture information of the base station 100 according to the picture and / or the laser scanning information, and the posture information includes at least the distance between the base station 100 and the robot 200 and / or the azimuth of the base station 100 relative to the robot 200.

[0116] The robot 200 provided in the embodiment of the present application determines that the base station 100 has been identified when the first pattern feature acquired by the camera 210 includes the first preset pattern feature and the second pattern feature acquired by the laser radar 220 includes the second preset pattern feature. By combining the camera 210 and the laser radar 220 to recognize the preset pattern 10 provided on the base station 100, the recognition accuracy of the base station 100 can be improved and misjudgment can be avoided. In addition, it is beneficial for the robot 200 to obtain accurate position information of the base station 100, so that the robot 200 can accurately reach the base station 100 for charging.

[0117] The robot 200 may be a lawn mowing robot, a sweeping robot, a delivery robot, a disinfection robot, or other types of robots.

[0118] In some embodiments, the control module 230 is configured to perform grayscale extraction on the image to obtain grayscale information, determine a grayscale variation trend based on the grayscale information, and determine the first reflectivity variation trend and / or the first size ratio sequence based on the grayscale variation trend, wherein the first reflectivity variation trend is opposite to the grayscale variation trend. And / or the control module 230 is configured to perform depth extraction on the image to obtain depth information, and determine the first depth variation trend based on the depth information.

[0119] In some embodiments, the control module 230 is configured to perform grayscale extraction on the entire area of ​​the image to obtain grayscale information, or identify the image to determine an image area, and perform grayscale extraction on the image area to obtain grayscale information.

[0120] In some embodiments, the control module 230 is configured to determine a laser intensity variation trend based on the laser intensity value and the scanning distance value, and to determine the second reflectivity variation trend and / or the second size ratio sequence based on the laser intensity variation trend, wherein the second reflectivity variation trend is the same as the laser intensity variation trend. Furthermore, the control module 230 is configured to determine the second depth variation trend based on the scanning distance value, the duration information, and the propagation speed of the laser.

[0121] In some embodiments, the control module 230 is configured to determine the azimuth angle of the base station relative to the robot based at least on the image and the actual size of the preset pattern, and determine the distance between the base station and the robot based on the laser scanning information.

[0122] In some embodiments, the control module 230 is configured to control the camera 210 to capture an image and obtain the corresponding capture time. The control module 230 is further configured to, when the base station is identified based on the first pattern feature of a certain image, send the capture time of the image to the base station 100, so that the base station 100 determines the brightness of the fill light 20 corresponding to the capture time and controls the brightness of the fill light 20 to be adjusted to the brightness corresponding to the capture time.

[0123] In some embodiments, the control module 230 may include a processing chip such as a CPU, a DSP, or an MCU.

[0124] Among them, the robot 200 corresponds to the aforementioned base station identification and positioning method. For more detailed description, please refer to the contents of each embodiment of the aforementioned base station identification and positioning method. The contents of the robot 200 and the aforementioned base station identification and positioning method can also be referenced to each other.

[0125] Please refer to Figure 16, which is a block diagram of a base station identification and positioning system 300 according to an embodiment of the present application. As shown in Figure 16, the base station identification and positioning system 300 includes a base station 100 and the aforementioned robot 200.

[0126] The base station 100 further includes a processor configured to control the fill light 20 to emit light and to adjust the brightness of the fill light 20. The processor is configured to, upon receiving the shooting time of an image sent by the robot 200, determine the brightness of the fill light 20 corresponding to the shooting time and control the fill light 20 to emit light at the specified brightness.

[0127] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is called and executed by a processor to implement the base station identification and positioning method provided in any of the aforementioned embodiments.

[0128] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0129] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0130] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] The above is an implementation method of the embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the embodiment of the present application. These improvements and modifications are also considered to be within the scope of protection of the present application.

Claims

1. A base station identification and positioning method, applied to robot identification and positioning base stations, characterized in that: The base station is provided with a preset pattern, the preset pattern has a first preset pattern feature and a second preset pattern feature, the first preset pattern feature and the second preset pattern feature are the same or different, and the robot includes a camera and a laser radar; The base station identification and positioning method comprises: Control the camera to shoot to obtain a picture, and control the laser radar to scan to obtain laser scanning information; Processing the image to determine a first pattern feature; Determine a second pattern feature according to the laser scanning information; When the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature, determining that the base station is identified; When the base station is identified, the position information of the base station is determined according to the image and / or the laser scanning information, and the position information at least includes the distance between the base station and the robot and / or the azimuth of the base station relative to the robot.

2. The base station identification and positioning method according to claim 1, characterized in that: The preset pattern includes at least two pattern partitions, and the at least two pattern partitions satisfy at least one of a first condition, a second condition and a third condition. The first condition includes that the at least two pattern partitions have different reflectivities under the photosensitive light of the camera and the laser emitted by the laser radar, and the reflectivities change according to a preset reflectivity change trend. The second condition includes that the ratio of the sizes of the at least two pattern partitions has a preset size ratio sequence. The third condition includes that the depths of the at least two pattern partitions change according to a preset depth change trend, and the depth of the pattern partition is the size in a direction perpendicular to the plane where the pattern partition is located. The first preset pattern feature includes at least one of the preset reflectivity change trend, the preset size ratio sequence and the preset depth change trend. The second preset pattern feature includes at least one of the preset reflectivity change trend, the preset size ratio sequence and the preset depth change trend.

3. The base station identification and positioning method according to claim 2, characterized in that: The first pattern feature includes at least one of a first reflectivity change trend, a first size ratio sequence, and a first depth change trend; and the processing of the image to determine the first pattern feature includes: Performing grayscale extraction on the image to obtain grayscale information; Determine a grayscale change trend according to the grayscale information; Determining the first reflectivity variation trend and / or the first size ratio sequence according to the grayscale variation trend, wherein the first reflectivity variation trend is opposite to the grayscale variation trend; and / or Performing depth extraction on the image to obtain depth information; determining the first depth change trend according to the depth information; Wherein, when the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature, determining that the base station is identified includes: When the first pattern feature satisfies at least one of the following characteristic conditions, the characteristic conditions including: the first reflectivity change trend includes the preset reflectivity change trend, the first size ratio sequence includes the preset size ratio sequence and the first depth change trend, and the second pattern feature includes the second preset pattern feature, it is determined that the base station is identified.

4. The base station identification and positioning method according to claim 3, characterized in that: The step of extracting grayscale from the image to obtain grayscale information includes: Performing grayscale extraction on the entire area of ​​the image to obtain grayscale information; or identifying the image to determine an image region; Grayscale extraction is performed on the image area to obtain grayscale information.

5. The base station identification and positioning method according to claim 2, characterized in that: The laser scanning information includes a laser intensity value and a scanning distance value, and / or duration information, the duration information includes an interval between the laser radar emitting the laser and receiving the laser, the second pattern feature includes at least one of a second reflectivity change trend, a second size ratio sequence, and a second depth change trend; the determining the second pattern feature according to the laser scanning information includes: Determine the laser intensity variation trend according to the laser intensity value and the scanning distance value; Determining the second reflectivity variation trend and / or the second size ratio sequence according to the laser intensity variation trend, wherein the second reflectivity variation trend is the same as the laser intensity variation trend; and / or Determining the second depth change trend according to the scanning distance value, the duration information, and the propagation speed of the laser; Wherein, when the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature, determining that the base station is identified includes: When the second pattern feature satisfies at least one of the following characteristic conditions, the characteristic conditions including: the second reflectivity change trend includes the preset reflectivity change trend, the second size ratio sequence includes the preset size ratio sequence and the second depth change trend, and the first pattern feature includes the first preset pattern feature, it is determined that the base station is identified.

6. The base station identification and positioning method according to claim 1, characterized in that: The determining the position information of the base station according to the image and / or the laser scanning information includes: Determining the azimuth angle of the base station relative to the robot at least according to the picture and the actual size of the preset pattern; The distance between the base station and the robot is determined according to the laser scanning information.

7. The base station identification and positioning method according to claim 1, characterized in that: The base station includes a fill light for emitting light with gradually changing brightness to adjust the ambient light brightness of the preset pattern; The controlling the camera to shoot to obtain a picture includes: Controlling the camera to shoot to obtain a picture and acquire the corresponding shooting time; The method further comprises: When the base station is identified according to the first pattern feature of a certain picture, the shooting time of the picture is sent to the base station, so that the base station determines the brightness of the fill light corresponding to the shooting time, and controls and adjusts the brightness of the fill light to the brightness corresponding to the shooting time.

8. The base station identification and positioning method according to claim 2, characterized in that: The at least two pattern partitions include at least one first pattern partition and at least one second pattern partition, the reflectivity of the camera's photosensitive light on the at least one first pattern partition is lower than the reflectivity on the at least one second pattern partition, the reflectivity of the laser emitted by the lidar on the at least one first pattern partition is lower than the reflectivity on the at least one second pattern partition, and the at least one first pattern partition and the at least one second pattern partition are alternately arranged.

9. The base station identification and positioning method according to claim 8, characterized in that: The at least one first pattern partition and the at least one second pattern partition are alternately arranged along a first preset direction, the width of the first pattern partition and the width of the second pattern partition are the same along the second preset direction, the second preset direction is perpendicular to the first preset direction, and the width of the first pattern partition and the width of the second pattern partition are both dimensions along the first preset direction; the preset size ratio sequence is a sequence composed of width ratios of each two adjacent pattern partitions in the at least two pattern partitions.

10. A robot, characterized in that: The robot is used in combination with a base station, the base station is provided with a preset pattern, the preset pattern has a first preset pattern feature and a second preset pattern feature, and the first preset pattern feature and the second preset pattern feature are the same or different; The robot comprises: Camera, for taking pictures; LiDAR, used for scanning to obtain laser scanning information; A control module is used to control the camera to shoot to obtain a picture, control the laser radar to scan to obtain laser scanning information, and process the picture to determine a first pattern feature, and determine a second pattern feature according to the laser scanning information. The control module is also used to determine that the base station is identified when the first pattern feature includes the first preset pattern feature and the second pattern feature includes the second preset pattern feature, and when the base station is identified, determine the posture information of the base station according to the picture and / or the laser scanning information, and the posture information at least includes the distance between the base station and the robot and / or the azimuth of the base station relative to the robot.

11. A base station identification and positioning system, characterized in that: The base station identification and positioning system comprises a base station and the robot as claimed in claim 10.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used for being called and executed by the processor to implement the base station identification and positioning method as described in any one of claims 1-9.

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